Biological Contagion Research Articles

Paradoxes in the coevolution of contagions and institutions.

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions.

Long ties accelerate noisy threshold-based contagions.

In widely used models of biological contagion, interventions that randomly rewire edges (generally making them 'longer') accelerate spread. However, recent work has argued that highly clustered, rather than random, networks facilitate the spread of threshold-based contagions, such as those motivated by myopic best response for adoption of new innovations, norms and products in games of strategic complement. Here we show that minor modifications to this model reverse this result, thereby harmonizing qualitative facts about how network structure affects contagion. We analyse the rate of spread over circular lattices with rewired edges and show that having a small probability of adoption below the threshold probability is enough to ensure that random rewiring accelerates the spread of a noisy threshold-based contagion. This conclusion is verified in simulations of empirical networks and remains valid with partial but frequent enough rewiring and when adoption decisions are reversible but infrequently so, as well as in high-dimensional lattice structures.

Interpretation of biosafety in endodontics by means of a literature review using PRISMA 2020 methodology

Biosafety is of great importance in all areas of health care, including endodontic procedures, since they represent a high risk of biological transmission of diseases such as AIDS and hepatitis, among others. The aim of the study was to interpret biosafety in endodontics by means of a literature review using PRISMA 2020 methodology. It was an observational, retrospective and descriptive study corresponding to the exploratory level, following the guidelines of the PRISMA 2020 methodology, combining the terms 'biosafety' and 'endodontics' in the PubMed and Google Scholar databases. The results highlighted the importance of biosafety in endodontic procedures and it was found that endodontics is used to treat different types of endodontic infections with high bacterial loads, so the correct use of biosafety barriers and protocols is necessary, since the area of dentistry has a high risk of biological contagion. It was also shown that biosafety comprises a set of rules, precautions and guidelines used in various scientific research and teaching activities to prevent risks or infections associated with exposure to agents that may be infectious or present significant biological, chemical or physical risks. It was concluded that endodontics and its procedures require strict compliance with biosafety protocols to ensure a high success rate and safeguard the health of the professional, the patient and the work team

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

Development and validation of a biological risk assessment tool among hospital personnel under COVID-19 pandemic conditions.

The need for a biological disease risk assessment method to prevent the contagion of these diseases, particularly among healthcare personnel, is crucial. Therefore, this study aimed to develop and validate a biological risk assessment tool for biological agents among hospital personnel under COVID-19 conditions. This cross-sectional study was performed on 301 employees in two hospitals. Firstly, we identified the items affecting the contagion of biological agents. Then, we computed the weight of the items using the Fuzzy Analytical Hierarchy Process (FAHP) method. We used the identified items and the estimated weights in the next step to develop a predictive equation. The outcome of this tool was the risk score of biological disease contagion. After that, we used the developed method to evaluate the biological risk of the participants. The ROC curve was also used to reveal accuracy of developed method. In this study, 29 items were identified and categorized into five dimensions, including environmental items, ventilation items, job items, equipment-related items, and organizational items. The weights of these dimensions were estimated at 0.172, 0.196, 0.255, 0.233, and 0.144, respectively. The final weight of items was used to develop a predictive equation. The area under ROC curves (AUC) was also calculated as 0.762 (95% CI: 0.704, 0.820) (p<0.001). The tools developed using these items had acceptable diagnostic accuracy for predicting the risk of biological diseases in health care. Therefore, one can apply it in identifying persons exposed to dangerous conditions.

Automated discovery of analytical models for epidemic dynamics on coevolving networks

Coevolving network systems provide a framework for modeling the diffusion of social and biological contagions on networks, where network structure is allowed to coevolve with the contagion dynamics, describing phenomena such as the spread of epidemics on social networks where individuals rewire their connectivity to avoid infection. Along with direct computational simulations, coevolving network systems are often formulated in terms of systems of ordinary differential equations in their descriptive statistics. The differential equation approach reduces the computational burden of analyzing the systems, but deriving equations becomes difficult as the underlying model becomes more complicated and the desire for accuracy increases. We present a sparse model identification approach that learns the nonlinear differential equations for coevolving network systems using data from computational simulations. Using this approach, we construct a data-driven system of equations for a coevolving SIS model that reproduces system behavior in both temporal evolution and dependence of steady states on system parameters. Furthermore, we also present the development of a numerical differentiation scheme that performs better for our application than the standard ones in use.

“It Has Made Me Think”: Engaging the Public with the History of Health in the Modern Irish Prison

Since the establishment of the modern prison system in the early nineteenth century, prisons and prisoners have been construed as sites of moral, social, and biological contagion. Historic and contemporary studies show that most prisoners experience severe health inequalities, higher rates of addiction and mental health issues, and lower life expectancy than the rest of the population. They also come from deprived social strata. Yet, these aspects of Irish penal history have been largely neglected in academia and popular histories. Our article discusses two public history projects—an art installation, The Trial, and a museum exhibition, Living Inside—that engaged different publics with the long history of health and welfare in Irish prisons. Developed by the research team on the Wellcome Trust Investigator Award “Prisoners, Medical Care and Entitlement to Health in England and Ireland, 1850-2000,” based at University College Dublin, the projects adopted different methodologies to engage their audiences and explore the experience and management of health and welfare in nineteenth- and twentieth-century Irish prisons. We further examine the different methodological approaches of each project, their varied aims and audiences, and the impacts reported by audiences and participants. The article also considers some of the challenges of doing this kind of public history, both in terms of working with marginalized communities and presenting research about difficult subjects to various audiences.

Assessing functional state of the body when wearing a reusable protective suit to minimize risks of contagion among medical personnel

Personal protective equipment has become the last line of protection for medical personnel during the pandemic of the new coronavirus infection since it allows minimizing risks of biological contagion. Given the existing staffing shortage, medical workers have to spend from 4 to 12 hours a day in the “red zone” where they necessarily wear personal protective equipment. Protective clothing is known to produce negative effects on functional state of the body and personnel’s working capacities. Assessment of up-to-date protective suits will allow developing recommendations on their suitable application bearing in mind a balance between necessary protection, providing favorable ergonomics, and reducing risks of adverse effects on functional state and working capacities. Our research aim was to hygienically assess health risks for medical workers who had to wear reusable protective suits. Our research object was a reusable suit made from polyether fabric with polyurethane membrane coating and antistatic threads. We performed an experiment aimed at evaluating thermal state of the body, psychophysiological state, and responses by the volunteers’ cardiorespiratory system in laboratory conditions during an 80 hour working shift under controlled microclimate. Participants in the experiment were questioned in order to assess suits’ ergonomics. Heat exchange dynamics and amount of changes in thermal physiological parameters caused by wearing a protective suit determined heat contents of volunteers’ bodies that conformed to optimal standard values. Data on psychophysiological and mental state taken in research dynamics didn’t have any statistically significant changes. Gas exchange indicators naturally grew during the “load” phase; however, there were no significant changes detected in any phase in the research. Hygienic assessment of the thermal state, functional state of the cardiovascular and respiratory systems, and psychophysio-logical indicators confirmed that wearing a protective suit was quite safe and didn’t involve any health risks for volunteers.

Оценка функционального состояния организма при эксплуатации защитного многоразового костюма как средства минимизации риска инфицирования медицинского персонала

Personal protective equipment has become the last line of protection for medical personnel during the pandemic of the new coronavirus infection since it allows minimizing risks of biological contagion. Given the existing staffing shortage, medical workers have to spend from 4 to 12 hours a day in the “red zone” where they necessarily wear personal protective equipment. Protective clothing is known to produce negative effects on functional state of the body and personnel’s working capacities. Assessment of up-to-date protective suits will allow developing recommendations on their suitable application bearing in mind a balance between necessary protection, providing favorable ergonomics, and reducing risks of adverse effects on functional state and working capacities. Our research aim was to hygienically assess health risks for medical workers who had to wear reusable protective suits. Our research object was a reusable suit made from polyether fabric with polyurethane membrane coating and antistatic threads. We performed an experiment aimed at evaluating thermal state of the body, psychophysiological state, and responses by the volunteers’ cardiorespiratory system in laboratory conditions during an 80 hour working shift under controlled microclimate. Participants in the experiment were questioned in order to assess suits’ ergonomics. Heat exchange dynamics and amount of changes in thermal physiological parameters caused by wearing a protective suit determined heat contents of volunteers’ bodies that conformed to optimal standard values. Data on psychophysiological and mental state taken in research dynamics didn’t have any statistically significant changes. Gas exchange indicators naturally grew during the “load” phase; however, there were no significant changes detected in any phase in the research. Hygienic assessment of the thermal state, functional state of the cardiovascular and respiratory systems, and psychophysio-logical indicators confirmed that wearing a protective suit was quite safe and didn’t involve any health risks for volunteers.

Countering the potential re-emergence of a deadly infectious disease—Information warfare, identifying strategic threats, launching countermeasures

Eradicated infectious diseases like smallpox can re-emerge through accident or the designs of bioterrorists, and cause heavy casualties. Presently, the populace is largely susceptible as only a small percentage is vaccinated, and their immunity is likely to have waned. And when the disease re-emerges, the susceptible individuals may be manipulated by disinformation on Social Media to refuse vaccines. Thus, a combination of countermeasures consisting of antiviral drugs and vaccines and a range of policies for their application need to be investigated. Opinions regarding whether to receive vaccines evolve over time through social exchanges via networks that overlap with but are not identical to the disease propagation networks. These couple the spread of the biological and information contagion and necessitate a joint investigation of the two. We develop a computationally tractable metapopulation epidemiological model that captures the joint spatio-temporal evolution of an infectious disease (e.g., smallpox, COVID-19) and opinion dynamics. Considering smallpox, the computations based on the model show that opinion dynamics have a substantial impact on the fatality count. Towards understanding how perpetrators are likely to seed the infection, we identify a) the initial distribution of infected individuals that maximize the overall fatality count; and b) which habitation structures are more vulnerable to outbreaks. We assess the relative efficacy of different countermeasures and conclude that a combination of vaccines and drugs minimize the fatalities, and by itself, drugs reduce fatalities more than the vaccine. Accordingly, we assess the impact of increase in the supply of drugs and identify the most effective among a collection of policies for administering of drugs for various parameter combinations. Many of the observed patterns are stable to variations of a diverse set of parameters. Our findings provide a quantitative foundation for various important elements of public health discourse that have largely been conducted qualitatively.

Cognitive and Medical Misanthropology: Corona Parties and Kolo Dancing by the Monument

This paper looks at so-called corona parties in Serbia, which can be seen as a specific paradigm of exhibiting irresponsible health behavior during an epidemic. The term refers to illegal gatherings of a large number of people in circumstances when all gatherings are restricted under anti-epidemic measures. A phenomenon similar to corona parties and co-ocurring with them in the Serbian socio-cultural and pandemic temporal context, is the dancing of the traditional kolo dance in public spaces. Both phenomena represent a conscious disregard for one's own health and of regulations introduced by the authorities, and at the same time an emphatic public display of indifference towards the epidemiological situation in the country, and rejection of the consequent legal restrictions on public life. The paper aims to establish the cultural background of such behavior, i.e. to ascertain its socio-cultural meaning. The indirect or direct endangerment of one’s own or other people’s health, particularly in a pandemic, can be seen as a misanthropic act. The cultural notions on which such irrational behavior is based are a consequence of a postmodernist relativization of previously existing socio-cultural discourse on science, and are counterintuitive. Behavior based on these notions is an irrational response to changes in socio-cultural reality due to COVID-19. The response is not only irrational but also ineffective, as it cannot eliminate the undesired consequences of the given situation, neither in terms of the illness itself, nor in terms of how it will be managed by those who have been put in charge by the government. Due to this, such behavior can also be seen simply as a deliberate defiance of rules. The misanthropic quality of the behavior of those who ignore anti-epidemic measures by dancing kolo in the streets or attending corona parties is evident in the conscious rejection of the principle of not harming others. Ignoring the possible health risks to themselves, they ignore the possible health risks to others, and thus become social factors of biological contagion. It is in this way that such behavior becomes the cause of the extension of the very state of socio-cultural reality against which it is supposed to be directed.

New scaling laws predicting turbulent particle pair diffusion, overcoming the limitations of the prevalent Richardson–Obukhov theory

Both the evolution of particle pair separation distance l in a turbulent flow and how different length scales affect l are major unresolved challenges. The reigning theory in this topic is that of Richardson and Obukhov (R-O theory). We propose a new theory of pair diffusion in homogeneous, isotropic turbulence hypothesizing that not only structures of size l, but much larger ones also induce significant pair separation—ignored in the R-O theory. We arrive at new scaling laws for the pair diffusivity K, leading to K∼lγ where γ depends on the size of the inertial subrange: for a short inertial subrange, we find from our simulations that K∼l1.44, and for an infinite inertial subrange, we find that K∼l1.556—these relations agree closely with data. We assert that the celebrated “R-O constant” gl is neither physically meaningful nor a constant as universally assumed; our theory leads to two new physically relevant constants: GK for pair diffusivity and Gl for pair separation—which asymptote to GK≈0.73 and Gl≈0.01 at high Reynolds numbers. We find that the particle dispersion is smaller by an order of magnitude compared to R-O prediction; this is significant in many applications such as sprays, and, in particular, the spread of biological contagions (e.g., COVID19) which persist longer and drift farther compared to R-O prediction. We find that the turbulent dispersion does not depend on the fine structure timescale—a striking result which would greatly facilitate turbulent diffusion modeling.

The epidemiology of moral bioenhancement

In their 2008 paper, Persson and Savulescu suggest that for moral bioenhancement (MBE) to be effective at eliminating the danger of ‘ultimate harm’ the intervention would need to be compulsory. This is because those most in need of MBE would be least likely to undergo the intervention voluntarily. By drawing on concepts and theories from epidemiology, this paper will suggest that MBE may not need to be universal and compulsory to be effective at significantly improving the collective moral standing of a human populace and reducing the threat of ultimate harm. It will identify similarities between the mechanisms that allow biological contagions (such as a virus) and behaviours (such as those concerned with ethical and unethical actions) to develop, spread, and be reinforced within a population. It will then go onto suggest that, just as with the epidemiological principle of herd immunity, if enough people underwent MBE to reach a minimum threshold then the incidence and spread of immoral behaviours could be significantly reduced, even in those who have not received MBE.

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

There is a rich history of models for the interaction of a biological contagion like influenza with the spread of related information such as an influenza vaccination campaign. Recent work on the spread of interacting contagions on networks has highlighted that these interacting contagions can have counter-intuitive interplay with network structure. Here we generalize one of these frameworks to tackle three important features of the spread of awareness and disease: one, we model the dynamics on highly clustered, cliquish, networks to mimic the role of workplaces and households; two, the awareness contagion affects the spread of the biological contagion by reducing its transmission rate where an aware or vaccinated individual is less likely to be infected; and three, the biological contagion also affects the spread of the awareness contagion but by increasing its transmission rate where an infected individual is more receptive and more likely to share information related to the disease. Under these conditions, we find that increasing network clustering, which is known to hinder disease spread, can actually allow them to sustain larger epidemics of the disease in models with awareness. This counter-intuitive result goes against the conventional wisdom suggesting that random networks are justifiable as they provide worst-case scenario forecasts. To further investigate this result, we provide a closed-form criterion based on a two-step branching process (i.e., the numbers of expected tertiary infections) to identify different regions in parameter space where the net effect of clustering and co-infection varies. Altogether, our results highlight once again the need to go beyond random networks in disease modeling and illustrate the type of analysis that is possible even in complex models of interacting contagions.

Monte Carlo basin bifurcation analysis

Many high-dimensional complex systems exhibit an enormously complex landscape of possible asymptotic states. Here, we present a numerical approach geared towards analyzing such systems. It is situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis. With our machine learning method, based on random sampling and clustering methods, we are able to characterize the different asymptotic states or classes thereof and even their basins of attraction. In order to do this, suitable, easy to compute, statistics of trajectories with randomly generated initial conditions and parameters are clustered by an algorithm such as DBSCAN. Due to its modular and flexible nature, our method has a wide range of possible applications in many disciplines. While typical applications are oscillator networks, it is not limited only to ordinary differential equation systems, every complex system yielding trajectories, such as maps or agent-based models, can be analyzed, as we show by applying it the Dodds–Watts model, a generalized SIRS-model, modeling social and biological contagion. A second order Kuramoto model, used, e.g. to investigate power grid dynamics, and a Stuart–Landau oscillator network, each exhibiting a complex multistable regime, are shown as well. The method is available to use as a package for the Julia language.

Long Ties Accelerate Noisy Threshold-based Contagions

Network structure can affect when and how widely new ideas, products, and behaviors are adopted. In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them longer) accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which an individual's behavior is described by a threshold number of adopting neighbors above which adoption occurs (i.e., complex contagions). Recent work has argued that highly clustered, rather than random, networks facilitate spread of these complex contagions. Here we show that minor modifications to this model, which make it more realistic, reverse this result: we allow very rare below-threshold adoption, i.e., rarely adoption occurs when there is only one adopting neighbor. To model the trade-off between long and edges we consider networks that are the union of cycle-power-k graphs and random graphs on n nodes. Allowing adoptions below threshold to occur with order 1/\sqrt(n) probability along some short cycle edges is enough to ensure that random rewiring accelerates spread. Simulations illustrate the robustness of these results to other commonly-posited models for noisy best-response behavior. Hypothetical interventions that randomly rewire existing edges or add random edges (versus adding short, triad-closing edges) in hundreds of empirical social networks reduce time to spread. This revised conclusion suggests that those wanting to increase spread should induce formation of long ties, rather than triad-closing ties. More generally, this highlights the importance of noise in game-theoretic analyses of behavior.

Design of Tunnel Inspection Robot for Large Diameter Sewers

The Singapore used water transport infrastructure comprises of three sewage systems: trunk sewer, link sewer network and the Deep Tunnel Sewerage System (DTSS). It is a solution towards Singapore’s long-term water needs such as used water collection, treatment, reclamation and disposal. Environmental conditions and activities in and outside the tunnels can lead to deterioration of tunnel assets such as liner cracking, dislocated joints or even collapsed sections over an extended period. Leaks from sewer tunnels may contaminate the surrounding land and pose risks to public health. In order to prolong the service life of sewers and to protect surrounding environment, inspecting the structural integrity of the tunnel is an essential part of infrastructure maintenance.The use of robots is one of the options being explored to assess underground spaces and to achieve enhanced inspection and maintenance capabilities. This option is desirable as it reduces the risk to humans resulting from prolonged incursions into a hazardous environment. The presence of biological contagions, hazardous and explosive gases (predominantly hydrogen sulphide, methane etc.) can pose a threat to the wellbeing of humans. Oxygen deprivation, absence of illumination and slippery conditions can further add to the risk level. In addition, automation promises greater reliability and manpower savings.Deploying robots into modern-day sewer systems are not without its engineering and technological challenges. Frequently there are restrictions to access through manhole openings. Access tunnels may have further structural obstructions to permit the use of launch support mechanisms. In addition, the depth of service tunnels approaches 50 m with distances between adjacent accesses points approaching 2 km. Keeping the robots supplied with power is challenging and with conflicting operational advantages and disadvantages between on-board battery and surface supplied power. The paper provides insights to the identification of design considerations and field trial for our tunnel inspection robot.

A rumor spreading model based on information entropy

Rumor spreading can have a significant impact on people’s lives, distorting scientific facts and influencing political opinions. With technologies that have democratized the production and reproduction of information, the rate at which misinformation can spread has increased significantly, leading many to describe contemporary times as a ‘post-truth era’. Research into rumor spreading has primarily been based on either model of social and biological contagion, or upon models of opinion dynamics. Here we present a comprehensive model that is based on information entropy, which allows for the incorporation of considerations like the role of memory, conformity effects, differences in the subjective propensity to produce distortions, and variations in the degree of trust that people place in each other. Variations in the degree of trust are controlled by a confidence factor β, while the propensity to produce distortions is controlled by a conservation factor K. Simulations were performed using a Barabási–Albert (BA) scale-free network seeded with a single piece of information. The influence of β and K upon the temporal evolution of the system was subsequently analyzed regarding average information entropy, opinion fragmentation, and the range of rumor spread. These results can aid in decision-making to limit the spread of rumors.

Dueling biological and social contagions

Numerous models explore how a wide variety of biological and social phenomena spread in social networks. However, these models implicitly assume that the spread of one phenomenon is not affected by the spread of another. Here, we develop a model of “dueling contagions”, with a particular illustration of a situation where one is biological (influenza) and the other is social (flu vaccination). We apply the model to unique time series data collected during the 2009 H1N1 epidemic that includes information about vaccination, flu, and face-to-face social networks. The results show that well-connected individuals are more likely to get vaccinated, as are people who are exposed to friends who get vaccinated or are exposed to friends who get the flu. Our dueling contagion model suggests that other epidemiological models may be dramatically underestimating the R0 of contagions. It also suggests that the rate of vaccination contagion may be even more important than the biological contagion in determining the course of the disease. These results suggest that real world and online platforms that make it easier to see when friends have been vaccinated (personalized vaccination campaigns) and when they get the flu (personalized flu warnings) could have a large impact on reducing the severity of epidemics. They also suggest possible benefits from understanding the coevolution of many kinds of dueling contagions.

Topological data analysis of contagion maps for examining spreading processes on networks.

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth’s surface; however, in modern contagions long-range edges—for example, due to airline transportation or communication media—allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct “contagion maps” that use multiple contagions on a network to map the nodes as a point cloud. By analyzing the topology, geometry, and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modeling, forecast, and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.